Application of machine learning for renewable energy prediction Ramraj S1,*, Karthick S1, Yashwant K2 1Assistant Professor, Dept. of Software Engineering, SRM Institute Of Technology, Chennai 2Student, Dept. of Software Engineering, SRM Institute Of Technology, Chennai *Corresponding Author: Ramraj S Assistant Professor Dept of Software Engineering, SRM Institute of Technology, Chennai Email: ramrajitsrm333@gmail.com
Online published on 16 October, 2018. Abstract Global horizontal irradiance or GHI is the amount of shortwave radiation acquired from above by a surface horizontal to the ground. It is of great significance in photovoltaic installation. GHI value is used to compute flat-panel PV output. In this paper we have discussed how to predict GHI value by using previous night's weather data and evaluated the predictions generated by various machine learning algorithm like Decision Tree Regression, Random Forest Regression and XGBoost Regression algorithms. Upon training we found that XGBoost Regressor was generating the best output of all the models we developed. We have evaluated the accuracy based on the metrics explained variance score. Top Keywords Decision Tree Algorithm, GHI, Random Forest Algorithm, XGBoost Algorithm. Top |